{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1c14a39b",
   "metadata": {},
   "source": [
    "# 金融市场基础第7节课书面作业\n",
    "学号：114847\n",
    "\n",
    "**作业内容：**  \n",
    "1. 附件是万科和阿里巴巴2016年股价数据，通过excel绘制在不同的投资比例下的CAMP曲线（要求比例从0~100%，取100个点）。  \n",
    "提示：得到两股票股价数据后，首先计算每天的收益率，通过excel函数得到每个股票收益率的均值E、方差σ以及两股票收益率的协方差Cov。通过不同的W1和W2（W1+W2=100%)，计算投资组合的Ep和σp，会得到100组数据，在excel中绘图。\n",
    "\n",
    "**答：**  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce6b270b",
   "metadata": {},
   "source": [
    "## 1 用Excel实现"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f951eb",
   "metadata": {},
   "source": [
    "excel文件参见：  \n",
    "https://gitee.com/dotzhen/financial-market-fundamentals/blob/master/class07/%E9%98%BF%E9%87%8C%E5%B7%B4%E5%B7%B4%E4%B8%8E%E4%B8%87%E7%A7%912016%E5%B9%B4%E8%82%A1%E4%BB%B7%E6%95%B0%E6%8D%AE.xlsx  \n",
    "截图如下："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99f11b6e",
   "metadata": {},
   "source": [
    "![fin07-1](https://gitee.com/dotzhen/cloud-notes/raw/master/fin07-1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90f30673",
   "metadata": {},
   "source": [
    "## 2 用python+pandas+matplotlib实现\n",
    "用python处理实现一下，我认为python比excel好用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "48be7dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "587e075c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = pd.read_csv('阿里巴巴与万科2016年股价数据.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19512a11",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>BABA</th>\n",
       "      <th>WK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04/01/2016</td>\n",
       "      <td>76.69</td>\n",
       "      <td>16.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05/01/2016</td>\n",
       "      <td>78.63</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06/01/2016</td>\n",
       "      <td>77.33</td>\n",
       "      <td>16.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>07/01/2016</td>\n",
       "      <td>72.72</td>\n",
       "      <td>16.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08/01/2016</td>\n",
       "      <td>70.80</td>\n",
       "      <td>15.92</td>\n",
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       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>23/12/2016</td>\n",
       "      <td>86.79</td>\n",
       "      <td>13.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>27/12/2016</td>\n",
       "      <td>87.54</td>\n",
       "      <td>13.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>28/12/2016</td>\n",
       "      <td>87.37</td>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>29/12/2016</td>\n",
       "      <td>87.33</td>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>30/12/2016</td>\n",
       "      <td>87.81</td>\n",
       "      <td>13.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date   BABA     WK\n",
       "0    04/01/2016  76.69  16.66\n",
       "1    05/01/2016  78.63  16.69\n",
       "2    06/01/2016  77.33  16.61\n",
       "3    07/01/2016  72.72  16.07\n",
       "4    08/01/2016  70.80  15.92\n",
       "..          ...    ...    ...\n",
       "247  23/12/2016  86.79  13.70\n",
       "248  27/12/2016  87.54  13.70\n",
       "249  28/12/2016  87.37  13.75\n",
       "250  29/12/2016  87.33  13.60\n",
       "251  30/12/2016  87.81  13.65\n",
       "\n",
       "[252 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7f4eec87",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['BABA_yr']=ds['BABA'].diff()\n",
    "ds['WK_yr']=ds['WK'].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "57c9e86a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>BABA</th>\n",
       "      <th>WK</th>\n",
       "      <th>BABA_yr</th>\n",
       "      <th>WK_yr</th>\n",
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       "  <tbody>\n",
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       "      <td>04/01/2016</td>\n",
       "      <td>76.69</td>\n",
       "      <td>16.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>05/01/2016</td>\n",
       "      <td>78.63</td>\n",
       "      <td>16.69</td>\n",
       "      <td>1.94</td>\n",
       "      <td>0.03</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06/01/2016</td>\n",
       "      <td>77.33</td>\n",
       "      <td>16.61</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>-0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>07/01/2016</td>\n",
       "      <td>72.72</td>\n",
       "      <td>16.07</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>-0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08/01/2016</td>\n",
       "      <td>70.80</td>\n",
       "      <td>15.92</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>-0.15</td>\n",
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       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>23/12/2016</td>\n",
       "      <td>86.79</td>\n",
       "      <td>13.70</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>27/12/2016</td>\n",
       "      <td>87.54</td>\n",
       "      <td>13.70</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>28/12/2016</td>\n",
       "      <td>87.37</td>\n",
       "      <td>13.75</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>29/12/2016</td>\n",
       "      <td>87.33</td>\n",
       "      <td>13.60</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>30/12/2016</td>\n",
       "      <td>87.81</td>\n",
       "      <td>13.65</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date   BABA     WK  BABA_yr  WK_yr\n",
       "0    04/01/2016  76.69  16.66      NaN    NaN\n",
       "1    05/01/2016  78.63  16.69     1.94   0.03\n",
       "2    06/01/2016  77.33  16.61    -1.30  -0.08\n",
       "3    07/01/2016  72.72  16.07    -4.61  -0.54\n",
       "4    08/01/2016  70.80  15.92    -1.92  -0.15\n",
       "..          ...    ...    ...      ...    ...\n",
       "247  23/12/2016  86.79  13.70    -0.01   0.30\n",
       "248  27/12/2016  87.54  13.70     0.75   0.00\n",
       "249  28/12/2016  87.37  13.75    -0.17   0.05\n",
       "250  29/12/2016  87.33  13.60    -0.04  -0.15\n",
       "251  30/12/2016  87.81  13.65     0.48   0.05\n",
       "\n",
       "[252 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "766aff08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "阿里巴巴股票收益的期望值：0.044303，方差：1.591550\n"
     ]
    }
   ],
   "source": [
    "baba_exp = ds['BABA_yr'].mean()\n",
    "baba_var = ds['BABA_yr'].std()\n",
    "print('阿里巴巴股票收益的期望值：%f，方差：%f'%(baba_exp,baba_var))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9546f7cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "万科股票收益的期望值：-0.011992，方差：0.364322\n"
     ]
    }
   ],
   "source": [
    "wk_exp = ds['WK_yr'].mean()\n",
    "wk_var = ds['WK_yr'].std()\n",
    "print('万科股票收益的期望值：%f，方差：%f'%(wk_exp,wk_var))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "29f9ec58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "阿里巴巴与万科股票收益的协方差：0.171111\n"
     ]
    }
   ],
   "source": [
    "bw_covar = ds['BABA_yr'].cov(ds['WK_yr'])\n",
    "print('阿里巴巴与万科股票收益的协方差：%f'%(bw_covar))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "397e9697",
   "metadata": {},
   "outputs": [],
   "source": [
    "w1 = np.arange(0,1.01,0.01)\n",
    "w2 = 1.0 - w1\n",
    "Erp = w1*baba_exp+w2*wk_exp\n",
    "sigmap = np.sqrt(w1*w1*baba_var*baba_var+w2*w2*wk_var*wk_var+2*w1*w2*bw_covar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "868855f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [sigmap[0],sigmap[50],sigmap[-1]]\n",
    "y = [Erp[0],Erp[50],Erp[-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4e35153f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5), dpi=150)\n",
    "plt.title('CAPM')\n",
    "plt.xlabel('$\\sigma$')\n",
    "plt.ylabel('E(r)')\n",
    "plt.plot(sigmap, Erp)\n",
    "plt.plot(x,y,'or')\n",
    "plt.annotate('100% Vanke', xy=(x[0], y[0]), xycoords='data', xytext=(+20, +0),\n",
    "             textcoords='offset points', arrowprops=dict(arrowstyle='->', connectionstyle=\"arc3,rad=.2\"))\n",
    "plt.annotate('50% Vanke+50% Alibaba', xy=(x[1], y[1]), xycoords='data', xytext=(-10, -20),\n",
    "             textcoords='offset points', arrowprops=dict(arrowstyle='->', connectionstyle=\"arc3,rad=.2\"))\n",
    "plt.annotate('100% Alibaba', xy=(x[2], y[2]), xycoords='data', xytext=(-100, 0),\n",
    "             textcoords='offset points', arrowprops=dict(arrowstyle='->', connectionstyle=\"arc3,rad=.2\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a02d8a0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
